Video is currently being generated by numerous software applications for display on a variety of computing devices. Video verification is often performed by software developers to gauge the quality of the generated video. Existing video verification methods include double-blind testing of participants in controlled settings. Other existing methods are automated. For example, in one method, every frame of a subject video clip is compared to frames from a known, good, reference video clip. Hardware limitations in existing computing devices limit the feasibility of this computationally complex method.
Other existing methods sample video frames from a video clip and compare the sampled frames to corresponding frames from the reference video clip. These methods, however, are limited in that existing frame comparison techniques only identify frame-level differences and ignore more subtle differences such as color tone and pixel-level detail. Further, most existing sampling techniques collect samples equally spaced in time thus limiting the sample set and reducing the efficacy of the verification.
Existing video verification methods further produce frequent false negatives which must be resolved through human review. For example, different graphics card drivers may render the same video clip with slight shading differences. Even though the shading differences may not be perceptible to human eyes, existing video verification techniques will flag the video clips as different if enough of the frames within the clip exhibit such shading differences.
Further, existing sampling techniques limit the quality of the sample set. With existing sampling techniques, there is an inability to verify particular content with equally spaced time samples. For example, titles and credits typically only appear at the beginning and/or end of a video clip and are thus hard to sample with existing sampling techniques. Additionally, transitions between clips and color tone effects such as sepia and grayscale are difficult to sample with existing sampling techniques.